OpenRaiser/Intern-Atlas huggingface dataset:
Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic
Venue: arXiv cs.LG 2019
Year: 2019

For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Recently, research in scenario planning benefits from utilizing generative adversarial networks (GANs) as generative models for operational scenario planning. In these scenarios, operators examine temporal as well as spatial influences of different energy sources on the grid. The analysis of how renewable energy resources affect the grid enables the operators to evaluate the stability and to identify potential weak points such as a limiting transformer. However, due to their novelty, there are limited studies on how well GANs model the underlying power distribution. This analysis is essential because, e.g., especially extreme situations with low or high power generation are required to evaluate grid stability. We conduct a comparative study of the Wasserstein distance, binary-cross-entropy loss, and a Gaussian copula as the baseline applied on two wind and two solar datasets with limited data compared to previous studies. Both GANs achieve good results considering the limited amount of data, but the Wasserstein GAN is superior in modeling temporal and spatial relations, and the power distribution. Besides evaluating the generated power distribution over all farms, it is essential to assess terrain specific distributions for wind scenarios. These terrain specific power distributions affect the grid by their differences in their generating power magnitude. Therefore, in a second study, we show that even when simultaneously learning distributions from wind parks with terrain specific patterns, GANs are capable of modeling these individualities also when faced with limited data.
Semantically Constrained Multilayer Annotation: The Case of Coreference
Venue: arXiv cs.CL 2019
Year: 2019

We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions. We argue that this allows coreference annotators to sidestep some of the challenges faced in other schemes, which do not enforce consistency with predicate-argument structure and vary widely in what kinds of mentions they annotate and how. The proposed approach is examined with a pilot annotation study and compared with annotations from other schemes.
Distributed Uplink Power Control in an Ultra-Dense Millimeter Wave Network: A Mean-field Game Approach
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, a novel mean-field game framework is proposed for uplink power control in an ultra-dense millimeter wave network. The proposed mean-field game considers the time evolution of the mobile users' orientations as well as the energy available in their batteries, under adaptive user association. The objective of each mobile user is then to find the optimal transmission power that maximizes its energy efficiency. The expression of the energy efficiency is analytically derived for the realistic case of a finite size network. Simulation results show that the proposed approach yields gains of up to 24%, in terms of energy efficiency, compared to a baseline in which the nodes transmit according to the path loss compensating power control policy.
Separation and Renaming in Nominal Sets
Venue: arXiv cs.LO 2019
Year: 2019

Nominal sets provide a foundation for reasoning about names. They are used primarily in syntax with binders, but also, e.g., to model automata over infinite alphabets. In this paper, nominal sets are related to nominal renaming sets, which involve arbitrary substitutions rather than permutations, through a categorical adjunction. In particular, the left adjoint relates the separated product of nominal sets to the Cartesian product of nominal renaming sets. Based on these results, we define the new notion of separated nominal automata. These automata can be exponentially smaller than classical nominal automata, if the semantics is closed under substitutions.
An Operational Guide to Monitorability
Venue: arXiv cs.LO 2019
Year: 2019

Monitorability delineates what properties can be verified at runtime. Although many monitorability definitions exist, few are defined explicitly in terms of the guarantees provided by monitors, i.e., the computational entities carrying out the verification. We view monitorability as a spectrum: the fewer monitor guarantees that are required, the more properties become monitorable. We present a monitorability hierarchy and provide operational and syntactic characterisations for its levels. Existing monitorability definitions are mapped into our hierarchy, providing a unified framework that makes the operational assumptions and guarantees of each definition explicit. This provides a rigorous foundation that can inform design choices and correctness claims for runtime verification tools.
Multi-Drone 3D Trajectory Planning and Scheduling in Drone Assisted Radio Access Networks
Venue: arXiv cs.NI 2019
Year: 2019

Drone base station (DBS) is a promising technique to extend wireless connections for uncovered users of terrestrial radio access networks (RAN). To improve user fairness and network performance, in this paper, we design 3D trajectories of multiple DBSs in the drone assisted radio access networks (DA-RAN) where DBSs fly over associated areas of interests (AoIs) and relay communications between the base station (BS) and users in AoIs. We formulate the multi-DBS 3D trajectory planning and scheduling as a mixed integer non-linear programming (MINLP) problem with the objective of minimizing the average DBS-to-user (D2U) pathloss. The 3D trajectory variations in both horizontal and vertical directions, as well as the state-of-the-art DBS-related channel models are considered in the formulation. To address the non-convexity and NP-hardness of the MINLP problem, we first decouple it into multiple integer linear programming (ILP) and quasi-convex sub-problems in which AoI association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DBS 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation and a search-based start slot scheduling are considered in the proposed algorithm to improve trajectory design performance and ensure inter-DBS distance constraint, respectively. Extensive simulations are conducted to investigate the impacts of DBS quantity, horizontal speed and initial trajectory on the trajectory planning results. Compared with the static DBS deployment, the proposed trajectory planning can achieve 10-15 dB reduction on average D2U pathloss, and reduce the D2U pathloss standard deviation by 68%, which indicate the improvements of network performance and user fairness.
One-Way Prototypical Networks
Venue: arXiv cs.LG 2019
Year: 2019

Few-shot models have become a popular topic of research in the past years. They offer the possibility to determine class belongings for unseen examples using just a handful of examples for each class. Such models are trained on a wide range of classes and their respective examples, learning a decision metric in the process. Types of few-shot models include matching networks and prototypical networks. We show a new way of training prototypical few-shot models for just a single class. These models have the ability to predict the likelihood of an unseen query belonging to a group of examples without any given counterexamples. The difficulty here lies in the fact that no relative distance to other classes can be calculated via softmax. We solve this problem by introducing a "null class" centered around zero, and enforcing centering with batch normalization. Trained on the commonly used Omniglot data set, we obtain a classification accuracy of .98 on the matched test set, and of .8 on unmatched MNIST data. On the more complex MiniImageNet data set, test accuracy is .8. In addition, we propose a novel Gaussian layer for distance calculation in a prototypical network, which takes the support examples' distribution rather than just their centroid into account. This extension shows promising results when a higher number of support examples is available.
Opportunistic Data Ferrying in Areas with Limited Information and Communications Infrastructure
Venue: arXiv cs.DC 2019
Year: 2019

Interest in smart cities is rapidly rising due to the global rise in urbanization and the wide-scale instrumentation of modern cities. Due to the considerable infrastructural cost of setting up smart cities and smart communities, researchers are exploring the use of existing vehicles on the roads as "message ferries" for the transport data for smart community applications to avoid the cost of installing new communication infrastructure. In this paper, we propose an opportunistic data ferry selection algorithm that strives to select vehicles that can minimize the overall delay for data delivery from a source to a given destination. Our proposed opportunistic algorithm utilizes an ensemble of online hiring algorithms, which are run together in passive mode, to select the online hiring algorithm that has performed the best in recent history. The proposed ensemble based algorithm is evaluated empirically using real-world traces from taxies plying routes in Shanghai, China, and its performance is compared against a baseline of four state-of-the-art online hiring algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm can reduce the overall delay compared to the baseline by an impressive 13% to 258%.
A Proactive Design to Detect Denial of Service Attacks Using SNMP-MIB ICMP Variables
Venue: arXiv cs.NI 2019
Year: 2019

Denial of Service (DOS) attack is one of the most attack that attract the cyber criminals which aims to reduce the network performance from doing its intended functions. Moreover, DOS Attacks can cause a huge damage on the data Confidentiality, Integrity and Availability. This paper introduced a system that detects the network traffic and varies the DOS attacks from normal traffic based on an adopted dataset. The results had shown that the adopted algorithms with the ICMP variables achieved a high accuracy percentage with approximately 99.6 in detecting ICMP Echo attack, HTTP Flood Attack, and Slowloris attack. Moreover, the designed model succeeded with a rate of 100 in varying normal traffic from various DOS attacks.
Software Adaptation and Generalization of Physically-Constrained Games
Venue: arXiv cs.HC 2019
Year: 2019

In this paper we provide a case study of the use of relatively sophisticated mathematics and algorithms to redefine and adapt a simple traditional game/puzzle to exploit the computational power of smart devices. The focus here is not so much on the end product as it is on the process and considerations underpinning its development. Ancillary results of the venture include generalizations of the circular-shift operator and examination of its computational complexity.
TACAM: Topic And Context Aware Argument Mining
Venue: arXiv cs.CL 2019
Year: 2019

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them. The main challenge in the argument recognition task, which is also known as argument mining, is that often sentences containing arguments are structurally similar to purely informative sentences without any stance about the topic. In fact, they only differ semantically. Most approaches use topic or search term information only for the first search step and therefore assume that arguments can be classified independently of a topic. We argue that topic information is crucial for argument mining, since the topic defines the semantic context of an argument. Precisely, we propose different models for the classification of arguments, which take information about a topic of an argument into account. Moreover, to enrich the context of a topic and to let models understand the context of the potential argument better, we integrate information from different external sources such as Knowledge Graphs or pre-trained NLP models. Our evaluation shows that considering topic information, especially in connection with external information, provides a significant performance boost for the argument mining task.
Service-Oriented Re-engineering of Legacy JEE Applications: Issues and Research Directions
Venue: arXiv cs.SE 2019
Year: 2019

Service-orientation views applications as orchestrations of independent software services that (1) implement functions that are reusable across many applications, (2) can be invoked remotely, and (3) are packaged to decouple potential callers from their implementation technology. As such, it enables organizations to develop quality applications faster than without services. Legacy applications are not service-oriented. Yet, they implement many reusable functions that could be exposed as \emph{services}. Organizations face three main issues when re-engineering legacy application to (re)use services: (1) to mine their existing applications for reusable functions that can become services, (2) to package those functions into services, and (3) to refactor legacy applications to invoke those services to ease future maintenance. In this paper, we explore these three issues and propose research directions to address them. We choose to focus on the service-oriented re-engineering of recent legacy object-oriented applications, and more specifically, on JEE applications, for several reasons. First, we wanted to focus on architectural challenges, and thus we choose to \textit{not} have to deal with programming language difference between source and target system. We chose JEE applications, in particular, because they embody the range of complexities that one can encounter in recent legacy applications, namely, multi-language systems, multi-tier applications, the reliance on external configuration files, and the reliance on frameworks and container services during runtime. These characteristics pose unique challenges for the three issues mentioned above.
Big-Data Clustering: K-Means or K-Indicators?
Venue: arXiv cs.LG 2019
Year: 2019

The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck with respect to the number of clusters K as this number grows in big data applications. In this work, we promote a closely related model called K-indicators model and construct an efficient, semi-convex-relaxation algorithm that requires no randomized initializations. We present extensive empirical results to show advantages of the new algorithm when K is large. In particular, using the new algorithm to start the K-means algorithm, without any replication, can significantly outperform the standard K-means with a large number of currently state-of-the-art random replications.
A Perspective on Objects and Systematic Generalization in Model-Based RL
Venue: arXiv cs.LG 2019
Year: 2019

In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment. Objects facilitate the modular reuse of prior knowledge and the combinatorial construction of such models. In this work, we argue that dynamically bound features (objects) do not simply emerge in connectionist models of the world. We identify several requirements that need to be fulfilled in overcoming this limitation and highlight corresponding inductive biases.
Adversarial Exploitation of Policy Imitation
Venue: arXiv cs.LG 2019
Year: 2019

This paper investigates a class of attacks targeting the confidentiality aspect of security in Deep Reinforcement Learning (DRL) policies. Recent research have established the vulnerability of supervised machine learning models (e.g., classifiers) to model extraction attacks. Such attacks leverage the loosely-restricted ability of the attacker to iteratively query the model for labels, thereby allowing for the forging of a labeled dataset which can be used to train a replica of the original model. In this work, we demonstrate the feasibility of exploiting imitation learning techniques in launching model extraction attacks on DRL agents. Furthermore, we develop proof-of-concept attacks that leverage such techniques for black-box attacks against the integrity of DRL policies. We also present a discussion on potential solution concepts for mitigation techniques.
Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

Noise and domain are important aspects of data quality for neural machine translation. Existing research focus separately on domain-data selection, clean-data selection, or their static combination, leaving the dynamic interaction across them not explicitly examined. This paper introduces a "co-curricular learning" method to compose dynamic domain-data selection with dynamic clean-data selection, for transfer learning across both capabilities. We apply an EM-style optimization procedure to further refine the "co-curriculum". Experiment results and analysis with two domains demonstrate the effectiveness of the method and the properties of data scheduled by the co-curriculum.
Style Transfer With Adaptation to the Central Objects of the Scene
Venue: arXiv cs.CV 2019
Year: 2019

Style transfer is a problem of rendering image with some content in the style of another image, for example a family photo in the style of a painting of some famous artist. The drawback of classical style transfer algorithm is that it imposes style uniformly on all parts of the content image, which perturbs central objects on the content image, such as faces or text, and makes them unrecognizable. This work proposes a novel style transfer algorithm which automatically detects central objects on the content image, generates spatial importance mask and imposes style non-uniformly: central objects are stylized less to preserve their recognizability and other parts of the image are stylized as usual to preserve the style. Three methods of automatic central object detection are proposed and evaluated qualitatively and via a user evaluation study. Both comparisons demonstrate higher quality of stylization compared to the classical style transfer method.
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications
Venue: arXiv cs.CL 2019
Year: 2019

Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 ontologies. The second demo classifies the Chinese online-shopping review into positive or negative.
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
Venue: arXiv cs.LG 2019
Year: 2019

In recent years, transfer learning gained particular interest in the field of vision and natural language processing. In the research field of vision, e.g., deep neural networks and transfer learning techniques achieve almost perfect classification scores within minutes. Nonetheless, these techniques are not yet widely applied in other domains. Therefore, this article identifies critical challenges and shows potential solutions for power forecasts in the field of renewable energies. It proposes a framework utilizing transfer learning techniques in wind power forecasts with limited or no historical data. On the one hand, this allows evaluating the applicability of transfer learning in the field of renewable energy. On the other hand, by developing automatic procedures, we assure that the proposed methods provide a framework that applies to domains in organic computing as well.
Convergence of Learning Dynamics in Stackelberg Games
Venue: arXiv cs.GT 2019
Year: 2019

This paper investigates the convergence of learning dynamics in Stackelberg games. In the class of games we consider, there is a hierarchical game being played between a leader and a follower with continuous action spaces. We establish a number of connections between the Nash and Stackelberg equilibrium concepts and characterize conditions under which attracting critical points of simultaneous gradient descent are Stackelberg equilibria in zero-sum games. Moreover, we show that the only stable critical points of the Stackelberg gradient dynamics are Stackelberg equilibria in zero-sum games. Using this insight, we develop a gradient-based update for the leader while the follower employs a best response strategy for which each stable critical point is guaranteed to be a Stackelberg equilibrium in zero-sum games. As a result, the learning rule provably converges to a Stackelberg equilibria given an initialization in the region of attraction of a stable critical point. We then consider a follower employing a gradient-play update rule instead of a best response strategy and propose a two-timescale algorithm with similar asymptotic convergence guarantees. For this algorithm, we also provide finite-time high probability bounds for local convergence to a neighborhood of a stable Stackelberg equilibrium in general-sum games. Finally, we present extensive numerical results that validate our theory, provide insights into the optimization landscape of generative adversarial networks, and demonstrate that the learning dynamics we propose can effectively train generative adversarial networks.